Machine and equipment assets, generally, are engineered to perform particular tasks as part of a business process. For example, assets can include, among other things and without limitation, industrial manufacturing equipment on a production line, drilling equipment for use in mining operations, wind turbines that generate electricity on a wind farm, transportation vehicles, and the like. As another example, assets may include healthcare machines and equipment that aid in diagnosing patients such as imaging devices (e.g., X-ray or MRI systems), monitoring devices, and the like. The design and implementation of these assets often takes into account both the physics of the task at hand, as well as the environment in which such assets are configured to operate.
Low-level software and hardware-based controllers have long been used to drive machine and equipment assets. However, the rise of inexpensive cloud computing, increasing sensor capabilities, and decreasing sensor costs, as well as the proliferation of mobile technologies have created opportunities for creating novel industrial and healthcare based assets with improved sensing technology and which are capable of transmitting data that can then be distributed throughout a network. As a consequence, there are new opportunities to enhance the business value of some assets through the use of novel industrial-focused hardware and software.
An operator typically associates virtual “tags” with sensors in order to identify parts of the asset for each sensor. For example, an oil rig may use tags identifying a motor, a hose, a drill line, etc., a wind turbine may have tags identifying a rotor, a drive train, a tower, etc., a gas turbine may have tags identifying an air compressor, a combustor, a burner, etc., and the like. That is, tags serve as identifiers for components that make up an asset. However, at present there is no standard naming convention for tags. As a result, different companies often have their own naming conventions for components of an asset. In addition, the same company may have multiple internal locations, each which may have assets that use different naming conventions, even where the assets are of the same type or from the same manufacturer.
In a complex asset environment, the proliferation of non-standardized tags significantly impedes the implementation of data monitoring and analytic solutions as a user must painstakingly associate each tagged component of each with the appropriate software input. Prior art approaches to this problem often involve highly time-consuming and error-prone manual user operations that do not scale well and result in unwanted data errors. Moreover, traditional computing techniques are incapable of parsing a large body of potential non-standardized tag records to automatically map different tags associated with the same sensors to one another. What is needed is a system and method capable of mapping together asset tags having different naming conventions.